'''simple docstring'''




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase					=1_6
__UpperCAmelCase					=3_2






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__ = 16							)      ->       List[str]:
    __lowerCamelCase            =    AutoTokenizer.from_pretrained('''bert-base-cased'''							)
    __lowerCamelCase            =    load_dataset('''glue'''							,    '''mrpc'''							)

    def tokenize_function(UpperCamelCase__							):
        # max_length=None => use the model max length (it's actually the default)
        __lowerCamelCase            =    tokenizer(examples['''sentence1''']							,    examples['''sentence2''']							,    truncation=UpperCamelCase__							,    max_length=UpperCamelCase__							)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        __lowerCamelCase            =    datasets.map(
            UpperCamelCase__							,    batched=UpperCamelCase__							,    remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,    )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    __lowerCamelCase            =    tokenized_datasets.rename_column('''label'''							,    '''labels'''							)

    def collate_fn(UpperCamelCase__							):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __lowerCamelCase            =    1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __lowerCamelCase            =    16
        elif accelerator.mixed_precision != "no":
            __lowerCamelCase            =    8
        else:
            __lowerCamelCase            =    None

        return tokenizer.pad(
            UpperCamelCase__							,    padding='''longest'''							,    max_length=UpperCamelCase__							,    pad_to_multiple_of=UpperCamelCase__							,    return_tensors='''pt'''							,    )

    # Instantiate dataloaders.
    __lowerCamelCase            =    DataLoader(
        tokenized_datasets['''train''']							,    shuffle=UpperCamelCase__							,    collate_fn=UpperCamelCase__							,    batch_size=UpperCamelCase__							)
    __lowerCamelCase            =    DataLoader(
        tokenized_datasets['''validation''']							,    shuffle=UpperCamelCase__							,    collate_fn=UpperCamelCase__							,    batch_size=UpperCamelCase__							)

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  __UpperCAmelCase					=mocked_dataloaders  # noqa: F811






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       str:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,    UpperCamelCase__							) == "1":
        __lowerCamelCase            =    2
    # Initialize accelerator
    __lowerCamelCase            =    Accelerator(cpu=args.cpu							,    mixed_precision=args.mixed_precision							)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __lowerCamelCase            =    config['''lr''']
    __lowerCamelCase            =    int(config['''num_epochs''']							)
    __lowerCamelCase            =    int(config['''seed''']							)
    __lowerCamelCase            =    int(config['''batch_size''']							)

    __lowerCamelCase            =    evaluate.load('''glue'''							,    '''mrpc'''							)

    # If the batch size is too big we use gradient accumulation
    __lowerCamelCase            =    1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        __lowerCamelCase            =    batch_size // MAX_GPU_BATCH_SIZE
        __lowerCamelCase            =    MAX_GPU_BATCH_SIZE

    set_seed(UpperCamelCase__							)
    __lowerCamelCase      ,    __lowerCamelCase            =    get_dataloaders(UpperCamelCase__							,    UpperCamelCase__							)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __lowerCamelCase            =    AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,    return_dict=UpperCamelCase__							)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    __lowerCamelCase            =    model.to(accelerator.device							)

    # Instantiate optimizer
    __lowerCamelCase            =    AdamW(params=model.parameters()							,    lr=UpperCamelCase__							)

    # Instantiate scheduler
    __lowerCamelCase            =    get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase__							,    num_warmup_steps=1_00							,    num_training_steps=(len(UpperCamelCase__							) * num_epochs) // gradient_accumulation_steps							,    )

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    accelerator.prepare(
        UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)

    # Now we train the model
    for epoch in range(UpperCamelCase__							):
        model.train()
        for step, batch in enumerate(UpperCamelCase__							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            __lowerCamelCase            =    model(**UpperCamelCase__							)
            __lowerCamelCase            =    outputs.loss
            __lowerCamelCase            =    loss / gradient_accumulation_steps
            accelerator.backward(UpperCamelCase__							)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        __lowerCamelCase            =    0
        for step, batch in enumerate(UpperCamelCase__							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            with torch.no_grad():
                __lowerCamelCase            =    model(**UpperCamelCase__							)
            __lowerCamelCase            =    outputs.logits.argmax(dim=-1							)
            __lowerCamelCase      ,    __lowerCamelCase            =    accelerator.gather((predictions, batch['''labels'''])							)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(UpperCamelCase__							) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    __lowerCamelCase            =    predictions[: len(eval_dataloader.dataset							) - samples_seen]
                    __lowerCamelCase            =    references[: len(eval_dataloader.dataset							) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=UpperCamelCase__							,    references=UpperCamelCase__							,    )

        __lowerCamelCase            =    metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""							,    UpperCamelCase__							)






def __lowerCAmelCase					(				)      ->       Optional[int]:
    __lowerCamelCase            =    argparse.ArgumentParser(description='''Simple example of training script.'''							)
    parser.add_argument(
        '''--mixed_precision'''							,    type=UpperCamelCase__							,    default=UpperCamelCase__							,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']							,    help='''Whether to use mixed precision. Choose'''
        '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
        '''and an Nvidia Ampere GPU.'''							,    )
    parser.add_argument('''--cpu'''							,    action='''store_true'''							,    help='''If passed, will train on the CPU.'''							)
    __lowerCamelCase            =    parser.parse_args()
    __lowerCamelCase            =    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(UpperCamelCase__							,    UpperCamelCase__							)


if __name__ == "__main__":
  main()

'''simple docstring'''




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__UpperCAmelCase					=logging.get_logger(__name__)




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Union[str, Any]				=["audio_values", "audio_mask"]






     def __init__(					self						:						List[str]    ,							a						:						List[Any]=20_48    ,							a						:						Optional[int]=1    ,							a						:						List[str]=[16, 16]    ,							a						:						int=1_28    ,							a						:						Dict=4_41_00    ,							a						:						str=86    ,							a						:						int=20_48    ,							a						:						int=0.0    ,							**a						:						Dict    ,							):





         """simple docstring"""



         super().__init__(
             feature_size=a    ,							sampling_rate=a    ,							padding_value=a    ,							**a    ,							)

         __lowerCamelCase            =    spectrogram_length
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_size
         __lowerCamelCase            =    feature_size // self.patch_size[1]
         __lowerCamelCase            =    n_fft
         __lowerCamelCase            =    sampling_rate // hop_length_to_sampling_rate
         __lowerCamelCase            =    sampling_rate
         __lowerCamelCase            =    padding_value
         __lowerCamelCase            =    mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2    ,							num_mel_filters=a    ,							min_frequency=0.0    ,							max_frequency=2_20_50.0    ,							sampling_rate=a    ,							norm='''slaney'''    ,							mel_scale='''slaney'''    ,							).T






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple    ,							a						:						np.array       ):





         """simple docstring"""



         __lowerCamelCase            =    spectrogram(
             a    ,							window_function(self.n_fft    ,							'''hann'''       )    ,							frame_length=self.n_fft    ,							hop_length=self.hop_length    ,							power=2.0    ,							mel_filters=self.mel_filters.T    ,							log_mel='''dB'''    ,							db_range=80.0    ,							)
         __lowerCamelCase            =    log_spec[:, :-1]
         __lowerCamelCase            =    log_spec - 20.0
         __lowerCamelCase            =    np.clip(log_spec / 40.0    ,							-2.0    ,							0.0       ) + 1.0
         return log_spec






     def __call__(					self						:						Union[str, Any]    ,							a						:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,							a						:						Optional[Union[str, TensorType]] = None    ,							a						:						Optional[bool] = True    ,							a						:						Optional[int] = None    ,							a						:						bool = False    ,							a						:						bool = False    ,							**a						:						int    ,							):





         """simple docstring"""



         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     '''This feature extractor is set to support sampling rate'''
                     f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
                     f""" with {self.sampling_rate} and not {sampling_rate}."""       )
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''       )

         __lowerCamelCase            =    isinstance(a    ,							np.ndarray       ) and len(raw_speech.shape       ) > 1
         if is_batched_numpy and len(raw_speech.shape       ) > 2:
             raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""       )
         __lowerCamelCase            =    is_batched_numpy or (
             isinstance(a    ,							(list, tuple)       ) and (isinstance(raw_speech[0]    ,							(np.ndarray, tuple, list)       ))
         )
         if is_batched:
             __lowerCamelCase            =    [np.asarray([speech]    ,							dtype=np.floataa       ).T for speech in raw_speech]
         elif not is_batched and not isinstance(a    ,							np.ndarray       ):
             __lowerCamelCase            =    np.asarray(a    ,							dtype=np.floataa       )
         elif isinstance(a    ,							np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
             __lowerCamelCase            =    raw_speech.astype(np.floataa       )
         # always return batch
         if not is_batched:
             __lowerCamelCase            =    [np.asarray([raw_speech]       ).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         __lowerCamelCase            =    [
             self._np_extract_fbank_features(waveform.squeeze()       ).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]    ,							a       ):
             __lowerCamelCase            =    [np.asarray(a    ,							dtype=np.floataa       ) for feature in audio_features]

         # Create audio attention mask
         __lowerCamelCase            =    max(
             [ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len for feature in audio_features]       )  # The maximum number of audio patches in a batch
         if return_attention_mask:
             __lowerCamelCase            =    [
                 (ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             __lowerCamelCase            =    np.array(a       ).astype(np.floataa       )

         # convert into correct format for padding
         __lowerCamelCase            =    max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         __lowerCamelCase            =    np.ones([len(a       ), 1, max_time_len, self.feature_size]       ).astype(np.floataa       )
         __lowerCamelCase            =    padded_audio_features * self.padding_value
         for i in range(len(a       )       ):
             __lowerCamelCase            =    audio_features[i]
             __lowerCamelCase            =    feature

         # return as BatchFeature
         if return_attention_mask:
             __lowerCamelCase            =    {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
         else:
             __lowerCamelCase            =    {'''audio_values''': padded_audio_features}

         __lowerCamelCase            =    BatchFeature(data=a    ,							tensor_type=a       )
         return encoded_inputs
